Alessandro Lazaric

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Sketched Newton-Raphson pdf

Rui Yuan, Robert M. Gower, Alessandro Lazaric, 2020, ArXiv.

Learning Reusable Skills through Self-Motivation

Andrea Bonarini, Alessandro Lazaric, Marcello Restelli, 2006, ICML 2006.

Semi-Supervised Apprenticeship Learning

Alessandro Lazaric, Michal Valko, Mohammad Ghavamzadeh, 2012, EWRL.

Exploiting easy data in online optimization

Alessandro Lazaric, Gergely Neu, Amir Sani, 2014, NIPS.

Rewards and errors in multi-arm bandits for interactive education

Alessandro Lazaric, Michal Valko, Yun-En Liu, 2016, NIPS 2016.

Transfer of task representation in reinforcement learning using policy-based proto-value functions

Eliseo Ferrante, Alessandro Lazaric, Marcello Restelli, 2008, AAMAS.

Fighting Boredom in Recommender Systems with Linear Reinforcement Learning

Alessandro Lazaric, Romain Warlop, Jérémie Mary, 2018, NeurIPS.

Pack only the essentials: Adaptive dictionary learning for kernel ridge regression

Daniele Calandriello, Alessandro Lazaric, Michal Valko, 2016, NIPS 2016.

Analysis of Kelner and Levin graph sparsification algorithm for a streaming setting pdf

Daniele Calandriello, Alessandro Lazaric, Michal Valko, 2016, ArXiv.

Reinforcement distribution in fuzzy Q-learning

Andrea Bonarini, Alessandro Lazaric, Marcello Restelli, 2009, Fuzzy Sets Syst..

Sequential Transfer in Multi-armed Bandit with Finite Set of Models

Alessandro Lazaric, Emma Brunskill, Mohammad Gheshlaghi Azar, 2013, NIPS.

Frequentist Regret Bounds for Randomized Least-Squares Value Iteration

Alessandro Lazaric, Matteo Pirotta, David Brandfonbrener, 2019, AISTATS.

Direct Policy Iteration with Demonstrations

Alessandro Lazaric, Jessica Chemali, A. Lazaric, 2015, IJCAI.

Classification-based Policy Iteration with a Critic

Bruno Scherrer, Alessandro Lazaric, Mohammad Ghavamzadeh, 2011, ICML.

A Learning Approach to Dynamic Coalition Formation

Andrea Bonarini, Alessandro Lazaric, Marcello Restelli, 2007 .

Conservative Exploration in Reinforcement Learning pdf

Alessandro Lazaric, Mohammad Ghavamzadeh, Matteo Pirotta, 2020, AISTATS.

Analysis of Nyström method with sequential ridge leverage scores

Daniele Calandriello, Alessandro Lazaric, Michal Valko, 2016, UAI.

A Provably Efficient Sample Collection Strategy for Reinforcement Learning pdf

Alessandro Lazaric, Michal Valko, Matteo Pirotta, 2020, ArXiv.

Meta-learning with Stochastic Linear Bandits pdf

Alessandro Lazaric, Massimiliano Pontil, Leonardo Cella, 2020, ICML.

Sparse multi-task reinforcement learning

Daniele Calandriello, Alessandro Lazaric, Marcello Restelli, 2014, Intelligenza Artificiale.

Multi-Bandit Best Arm Identification

Alessandro Lazaric, Sébastien Bubeck, Mohammad Ghavamzadeh, 2011, NIPS.

Incremental Skill Acquisition for Self-motivated Learning Animats

Andrea Bonarini, Alessandro Lazaric, Marcello Restelli, 2006, SAB.

Improved Algorithms for Conservative Exploration in Bandits pdf

Alessandro Lazaric, Mohammad Ghavamzadeh, Matteo Pirotta, 2020, AAAI.

Open Problem: Approximate Planning of POMDPs in the class of Memoryless Policies pdf

Kamyar Azizzadenesheli, Anima Anandkumar, Alessandro Lazaric, 2016, COLT.

Proceedings of the Nineteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2016

Peter L. Bartlett, Alessandro Lazaric, Ronald Ortner, 2016 .

Risk-Aversion in Multi-armed Bandits

Alessandro Lazaric, Rémi Munos, Amir Sani, 2012, NIPS.

Improved Learning Complexity in Combinatorial Pure Exploration Bandits

Peter L. Bartlett, Alessandro Lazaric, Ronald Ortner, 2016, AISTATS.

Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods

Andrea Bonarini, Alessandro Lazaric, Marcello Restelli, 2007, NIPS.

Stochastic Optimization of a Locally Smooth Function under Correlated Bandit Feedback pdf

Alessandro Lazaric, Emma Brunskill, Mohammad Gheshlaghi Azar, 2014, ArXiv.

Thompson Sampling for Linear-Quadratic Control Problems

Alessandro Lazaric, Marc Abeille, A. Lazaric, 2017, AISTATS.

Analysis of a Classification-based Policy Iteration Algorithm

Alessandro Lazaric, Rémi Munos, Mohammad Ghavamzadeh, 2010, ICML.

Hybrid Stochastic-Adversarial On-line Learning

Alessandro Lazaric, Rémi Munos, R. Munos, 2009, COLT.

Exploration – Exploitation in MDPs with Options pdf

Alessandro Lazaric, Ronan Fruit, Csaba Szepesvari, 2016 .

LEAP: Learning Entities Adaptive Partitioning.

Andrea Bonarini, Alessandro Lazaric, Marcello Restelli, 2005, NIPS 2005.

Finite-Sample Analysis of LSTD

Alessandro Lazaric, Rémi Munos, Mohammad Ghavamzadeh, 2010, ICML.

Efficient Optimistic Exploration in Linear-Quadratic Regulators via Lagrangian Relaxation pdf

Alessandro Lazaric, Marc Abeille, A. Lazaric, 2020, ICML.

Adversarial Attacks on Linear Contextual Bandits pdf

Olivier Teytaud, Alessandro Lazaric, Matteo Pirotta, 2020, NeurIPS.

Transfer of samples in batch reinforcement learning

Andrea Bonarini, Alessandro Lazaric, Marcello Restelli, 2008, ICML '08.

Active Exploration in Markov Decision Processes pdf

Alessandro Lazaric, Jean Tarbouriech, A. Lazaric, 2019, AISTATS.

Regret Minimization in MDPs with Options without Prior Knowledge

Alessandro Lazaric, Emma Brunskill, Matteo Pirotta, 2017, NIPS.

Learning Optimal Policies with State Aggregation

Andrea Bonarini, Alessandro Lazaric, Marcello Restelli, 2005 .

Improved Analysis of UCRL2 with Empirical Bernstein Inequality pdf

Alessandro Lazaric, Matteo Pirotta, Ronan Fruit, 2020, ArXiv.

Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits

Alessandro Lazaric, Alexandra Carpentier, Mohammad Ghavamzadeh, 2011, ALT.

Batch Reinforcement Learning for Controlling a Mobile Wheeled Pendulum Robot

Andrea Bonarini, Alessandro Lazaric, Marcello Restelli, 2008, IFIP AI.

Workshop summary: On-line learning with limited feedback

Peter Auer, Alessandro Lazaric, Csaba Szepesvári, 2009, ICML '09.

Reinforcement Learning in Complex Environments Through Multiple Adaptive Partitions

Andrea Bonarini, Alessandro Lazaric, Marcello Restelli, 2007, AI*IA.

Reinforcement Learning of Contextual MDPs using Spectral Methods pdf

Kamyar Azizzadenesheli, Anima Anandkumar, Alessandro Lazaric, 2016, ArXiv.

A truthful learning mechanism for contextual multi-slot sponsored search auctions with externalities

Alessandro Lazaric, Nicola Gatti, Francesco Trovò, 2012, EC '12.

LSTD with Random Projections

Alessandro Lazaric, Rémi Munos, Mohammad Ghavamzadeh, 2010, NIPS.

Regularized Least Squares Temporal Difference Learning with Nested ℓ2 and ℓ1 Penalization

Matthew W. Hoffman, Alessandro Lazaric, Rémi Munos, 2011, EWRL.

Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence

Alessandro Lazaric, Mohammad Ghavamzadeh, Victor Gabillon, 2012, NIPS.

Word-order Biases in Deep-agent Emergent Communication

Eugene Kharitonov, Alessandro Lazaric, Rahma Chaabouni, 2019, ACL.

Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning

Alessandro Lazaric, Ronald Ortner, Matteo Pirotta, 2018, ICML.

Distributed Adaptive Sampling for Kernel Matrix Approximation pdf

Daniele Calandriello, Alessandro Lazaric, Michal Valko, 2017, AISTATS.

Transfer from Multiple MDPs

Alessandro Lazaric, Marcello Restelli, A. Lazaric, 2011, NIPS.

Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes

Alessandro Lazaric, Matteo Pirotta, Ronan Fruit, 2018, NeurIPS.

Exploration Bonus for Regret Minimization in Undiscounted Discrete and Continuous Markov Decision Processes pdf

Alessandro Lazaric, Matteo Pirotta, Ronan Fruit, 2018, ArXiv.

Learning Adaptive Exploration Strategies in Dynamic Environments Through Informed Policy Regularization pdf

Ludovic Denoyer, Alessandro Lazaric, Nicolas Usunier, 2020, ArXiv.

Bifurcation Analysis of Reinforcement Learning Agents

Alessandro Lazaric, Marcello Restelli, Fabio Dercole, 2008 .

Finite-Sample Analysis of Lasso-TD

Matthew W. Hoffman, Alessandro Lazaric, Rémi Munos, 2011, ICML.

Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration pdf

Mykel J. Kochenderfer, Alessandro Lazaric, Emma Brunskill, 2020, NeurIPS.

Bifurcation Analysis of Reinforcement Learning Agents in the Selten's Horse Game

Alessandro Lazaric, Marcello Restelli, Enrique Munoz de Cote, 2007, Adaptive Agents and Multi-Agents Systems.

Reinforcement Learning of POMDPs using Spectral Methods

Kamyar Azizzadenesheli, Anima Anandkumar, Alessandro Lazaric, 2016, COLT.

Least-Squares Methods for Policy Iteration

Bart De Schutter, Robert Babuska, Lucian Busoniu, 2012, Reinforcement Learning.

Experimental results : Reinforcement Learning of POMDPs using Spectral Methods pdf

Kamyar Azizzadenesheli, Anima Anandkumar, Alessandro Lazaric, 2017, ArXiv.

An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits pdf

Alessandro Lazaric, Marcello Restelli, Matteo Pirotta, 2020, NeurIPS.

Reinforcement Learning in Rich-Observation MDPs using Spectral Methods

Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar, 2016, 1611.03907.

Learning to cooperate in multi-agent social dilemmas

Alessandro Lazaric, Marcello Restelli, Enrique Munoz de Cote, 2006, AAMAS '06.

Conservative and Greedy Approaches to Classification-Based Policy Iteration

Alessandro Lazaric, Mohammad Ghavamzadeh, 2012, AAAI.

Online Stochastic Optimization under Correlated Bandit Feedback

Alessandro Lazaric, Emma Brunskill, Mohammad Gheshlaghi Azar, 2014, ICML.

Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs

Alessandro Lazaric, Matteo Pirotta, Ronan Fruit, 2019, NeurIPS.

Best-Arm Identification in Linear Bandits

Alessandro Lazaric, Rémi Munos, Marta Soare, 2014, NIPS.

Second-Order Kernel Online Convex Optimization with Adaptive Sketching

Daniele Calandriello, Alessandro Lazaric, Michal Valko, 2017, ICML.

Regret Bounds for Learning State Representations in Reinforcement Learning

Alessandro Lazaric, Ronald Ortner, Matteo Pirotta, 2019, NeurIPS.

Towards Automated Bargaining in Electronic Markets: A Partially Two-Sided Competition Model

Alessandro Lazaric, Marcello Restelli, Nicola Gatti, 2008, AMEC/TADA.

Parallel Higher Order Alternating Least Square for Tensor Recommender System

Alessandro Lazaric, Romain Warlop, Jérémie Mary, 2017, AAAI Workshops.

Reinforcement learning in extensive form games with incomplete information: the bargaining case study

Alessandro Lazaric, Enrique Munoz de Cote, Nicola Gatti, 2007, AAMAS '07.

Efficient Second-Order Online Kernel Learning with Adaptive Embedding

Daniele Calandriello, Alessandro Lazaric, Michal Valko, 2017, NIPS.

Improving Batch Reinforcement Learning Performance through Transfer of Samples

Andrea Bonarini, Alessandro Lazaric, Marcello Restelli, 2008, STAIRS.

Transfer in Reinforcement Learning: A Framework and a Survey

Alessandro Lazaric, A. Lazaric, 2012, Reinforcement Learning.

A Novel Confidence-Based Algorithm for Structured Bandits pdf

Alessandro Lazaric, Marcello Restelli, Andrea Tirinzoni, 2020, AISTATS.

Improved Sample Complexity for Incremental Autonomous Exploration in MDPs pdf

Alessandro Lazaric, Michal Valko, Matteo Pirotta, 2020, NeurIPS.

Limiting Extrapolation in Linear Approximate Value Iteration

Mykel J. Kochenderfer, Alessandro Lazaric, Emma Brunskill, 2019, NeurIPS.

Near-linear Time Gaussian Process Optimization with Adaptive Batching and Resparsification

Daniele Calandriello, Lorenzo Rosasco, Luigi Carratino, 2020, ICML.

Yahtzee: a Large Stochastic Environment for RL Benchmarks

Andrea Bonarini, Alessandro Lazaric, Marcello Restelli, 2005, NIPS 2005.

Incremental Spectral Sparsification for Large-Scale Graph-Based Semi-Supervised Learning pdf

Daniele Calandriello, Alessandro Lazaric, Michal Valko, 2016, ArXiv.

Finite-sample Analysis of Bellman Residual Minimization

Alessandro Lazaric, Rémi Munos, Mohammad Ghavamzadeh, 2010, ACML.

No-Regret Exploration in Goal-Oriented Reinforcement Learning

Alessandro Lazaric, Michal Valko, Matteo Pirotta, 2019, ICML.

On the usefulness of opponent modeling: the Kuhn Poker case study

Alessandro Lazaric, Marcello Restelli, Mario Quaresimale, 2008, AAMAS.

Regret Bounds for Reinforcement Learning with Policy Advice pdf

Alessandro Lazaric, Emma Brunskill, Mohammad Gheshlaghi Azar, 2013, ECML/PKDD.

Maximum Entropy Semi-Supervised Inverse Reinforcement Learning

Alessandro Lazaric, Michal Valko, Mohammad Ghavamzadeh, 2015, IJCAI.

MESSI: Maximum Entropy Semi-Supervised Inverse Reinforcement Learning

Alessandro Lazaric, Michal Valko, Mohammad Ghavamzadeh, 2014, NIPS 2014.

Piecewise constant reinforcement learning for robotic applications

Andrea Bonarini, Alessandro Lazaric, Marcello Restelli, 2007, ICINCO-ICSO.

A Dantzig Selector Approach to Temporal Difference Learning

Matthieu Geist, Bruno Scherrer, Alessandro Lazaric, 2012, ICML.

Finite-sample analysis of least-squares policy iteration

Alessandro Lazaric, Rémi Munos, Mohammad Ghavamzadeh, 2012, J. Mach. Learn. Res..

Learning with stochastic inputs and adversarial outputs

Alessandro Lazaric, Rémi Munos, R. Munos, 2012, J. Comput. Syst. Sci..

Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems

Alessandro Lazaric, Marc Abeille, A. Lazaric, 2018, ICML.

Active Learning for Accurate Estimation of Linear Models

Alessandro Lazaric, Carlos Riquelme, Mohammad Ghavamzadeh, 2017, ICML.

Concentration Inequalities for Multinoulli Random Variables pdf

Alessandro Lazaric, Matteo Pirotta, Ronan Fruit, 2020, ArXiv.

Truthful learning mechanisms for multi-slot sponsored search auctions with externalities

Nicola Gatti, Alessandro Lazaric, Francesco Trovò, 2012, Artif. Intell..

Learning Driving Tasks in a Racing Game Using Reinforcement Learning

Alessandro Lazaric, Pier Luca, A. Lazaric, 2007 .

A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning

Alessandro Lazaric, Nicolas Usunier, Nicolas Carion, 2019, NeurIPS.

Improved Large-Scale Graph Learning through Ridge Spectral Sparsification

Daniele Calandriello, Alessandro Lazaric, Michal Valko, 2018, ICML.

Trading off Rewards and Errors in Multi-Armed Bandits

Alessandro Lazaric, Michal Valko, Yun-En Liu, 2017, AISTATS.

Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret pdf

Daniele Calandriello, Lorenzo Rosasco, Luigi Carratino, 2019, COLT.

Rotting bandits are no harder than stochastic ones

Alessandro Lazaric, Michal Valko, Alexandra Carpentier, 2018, AISTATS.

Active Model Estimation in Markov Decision Processes

Alessandro Lazaric, Mohammad Ghavamzadeh, Matteo Pirotta, 2020, UAI.

The replacement bootstrap for dependent data

Alessandro Lazaric, Daniil Ryabko, Amir Sani, 2015, 2015 IEEE International Symposium on Information Theory (ISIT).

A single algorithm for both restless and rested rotting bandits

Alessandro Lazaric, Michal Valko, Pierre Ménard, 2020, AISTATS.

Linear Thompson Sampling Revisited pdf

Alessandro Lazaric, Marc Abeille, A. Lazaric, 2016, AISTATS.

Learning Near Optimal Policies with Low Inherent Bellman Error

Alessandro Lazaric, Emma Brunskill, Mykel Kochenderfer, 2020, ICML.

Improving Cooperation among Self-Interested Reinforcement Learning Agents

Andrea Bonarini, Alessandro Lazaric, Marcello Restelli, 2005 .

Bayesian Multi-Task Reinforcement Learning

Alessandro Lazaric, Mohammad Ghavamzadeh, A. Lazaric, 2010, ICML.

Adaptive Multi-Goal Exploration pdf

Alessandro Lazaric, Michal Valko, Matteo Pirotta, 2021, ArXiv.

Differentially Private Exploration in Reinforcement Learning with Linear Representation pdf

Alessandro Lazaric, Matteo Pirotta, Paul Luyo, 2021, ArXiv.

Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning pdf

Alessandro Lazaric, Rob Fergus, Denis Yarats, 2021, ICLR.

Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret pdf

Alessandro Lazaric, Simon S. Du, Matteo Pirotta, 2021, NeurIPS.

A Unified Framework for Conservative Exploration pdf

Alessandro Lazaric, Liwei Wang, Simon S. Du, 2021, ArXiv.

Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection pdf

Alessandro Lazaric, Marcello Restelli, Aldo Pacchiano, 2021, NeurIPS.

Reinforcement Learning with Prototypical Representations

Alessandro Lazaric, Rob Fergus, Denis Yarats, 2021, ICML.

A Fully Problem-Dependent Regret Lower Bound for Finite-Horizon MDPs pdf

Alessandro Lazaric, Matteo Pirotta, Andrea Tirinzoni, 2021, ArXiv.

Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model

Alessandro Lazaric, Matteo Pirotta, Jean Tarbouriech, 2021, ALT.

Direct then Diffuse: Incremental Unsupervised Skill Discovery for State Covering and Goal Reaching pdf

Ludovic Denoyer, Alessandro Lazaric, Jean Tarbouriech, 2021, ICLR.

A general sample complexity analysis of vanilla policy gradient pdf

Alessandro Lazaric, Robert M. Gower, Rui Yuan, 2021, ArXiv.

Top K Ranking for Multi-Armed Bandit with Noisy Evaluations pdf

Alessandro Lazaric, Vashist Avadhanula, Evrard Garcelon, 2021, ArXiv.

Leveraging Good Representations in Linear Contextual Bandits

Alessandro Lazaric, Marcello Restelli, Matteo Pirotta, 2021, ICML.

No-Regret Exploration in Goal-Oriented Reinforcement Learning

Alessandro Lazaric, Michal Valko, Matteo Pirotta, 2020, ICML.

Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration pdf

Mykel J. Kochenderfer, Alessandro Lazaric, Emma Brunskill, 2020, NeurIPS.

Analysis of Nyström method with sequential ridge leverage score sampling

Daniele Calandriello, Alessandro Lazaric, Michal Valko, 2016, UAI 2016.

Frequentist Regret Bounds for Randomized Least-Squares Value Iteration

Alessandro Lazaric, Matteo Pirotta, David Brandfonbrener, 2020, AISTATS.

Linear Thompson Sampling Revisited

Alessandro Lazaric, Marc Abeille, A. Lazaric, 2016, International Conference on Artificial Intelligence and Statistics.

Large-scale semi-supervised learning with online spectral graph sparsification

Daniele Calandriello, Alessandro Lazaric, Michal Valko, 2015, ICML 2015.

Self-Development Framework for Reinforcement Learning Agents

Andrea Bonarini, Alessandro Lazaric, A. Lazaric, 2006 .

LQG for Portfolio Optimization

Alessandro Lazaric, Marc Abeille, A. Lazaric, 2016, 1611.00997.